A QuantumInspired Ensemble Method and QuantumInspired Forest Regressors
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Proceedings of the Ninth Asian Conference on Machine Learning, PMLR 77:8196, 2017.
Abstract
We propose a QuantumInspired Subspace(QIS) Ensemble Method for generating feature ensembles based on feature selections. We assign each principal component a Fraction Transition Probability as its probability weight based on Principal Component Analysis and quantum interpretations. In order to generate the feature subset for each base regressor, we select a feature subset from principal components based on Fraction Transition Probabilities. The idea originating from quantum mechanics can encourage ensemble diversity and the accuracy simultaneously. We incorporate QuantumInspired Subspace Method into Random Forest and propose QuantumInspired Forest. We theoretically prove that the quantum interpretation corresponds to the first order approximation of ensemble regression. We also evaluate the empirical performance of QuantumInspired Forest and Random Forest in multiple hyperparameter settings. QuantumInspired Forest proves the significant robustness of the default hyperparameters on most data sets. The contribution of this work is twofold, a novel ensemble regression algorithm inspired by quantum mechanics and the theoretical connection between quantum interpretations and machine learning algorithms.
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